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Researchers developed a cascaded deep network to enhance under-display Time-of-Flight (UD-ToF) depth maps. This method addresses blurring and accuracy issues in full-screen devices, improving depth sensing quality.

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Area of Science:

Computer Vision
Computational Imaging
Sensor Technology

Background:

Under-display imaging is gaining traction for full-screen devices.
Under-display Time-of-Flight (UD-ToF) cameras offer depth sensing but suffer from reduced image quality, signal-to-noise ratio, and ranging accuracy.

Purpose of the Study:

To propose a novel cascaded deep network for improving UD-ToF depth map quality.
To address the challenges of noise, blurring, and accuracy reduction in UD-ToF sensing.

Main Methods:

A two-subnet cascaded deep network was designed.
The first subnet employs a complex-valued network in the raw domain for joint denoising, deblurring, and raw measurement enhancement.
The second subnet refines depth maps in the depth domain using a multi-scale depth enhancement block (MSDEB).
Real and synthetic UD-ToF datasets were created for training and evaluation.

Main Results:

The proposed network effectively denoises and deburs UD-ToF data.
Depth map accuracy and signal-to-noise ratio are significantly improved.
Quantitative and qualitative evaluations show superior performance compared to state-of-the-art methods.

Conclusions:

The developed cascaded deep network offers a robust solution for enhancing UD-ToF depth maps.
This technology has the potential to advance the practical application of full-screen devices with integrated depth sensing.